3/24/2021 0 Comments How To Analyze Eeg Data
EEG based heroin addiction studies mainly rely on power and coherence analysis tools 2, 3.By continuing to use this site you agree to our use of cookies.
![]() The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 145 Hz frequency domain. ![]() Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Clinical studies have showed that chronic heroin abuse led to abnormal functional brain organization 2 4. As functional neuroimaging technologies develop, it is possible to reveal the brain functional alternations caused by heroin addiction. Functional magnetic resonance imaging (fMRI) and EEG recordings have been widely applied to study Alzheimers disease, depression, schizophrenia 5, as well as the abnormalities of brain functional system (BFS) induced by chronic heroin abuse 2, 6. Studies implied that the gray matter density was reduced in the prefrontal and temporal cortices of the heroin addicts using resting-state fMRI data 7 9; the regional homogeneities of the bilateral medial orbitofrontal cortex (OFC), bilateral dorsal medial thalamus, bilateral cuneus, and lingual gyrus were diminished in heroin addicts 1. Nevertheless, the low temporal resolution of fMRI cannot provide sufficient brain information in the time-frequency domain. EEG uses non-invasive electrodes to measure the electrophysiological signals accompanying real-time brain activities; it can be easily administered in either research or clinical settings 10. Functional effective network based EEG is becoming one of the main tools to study the BFS of neurological disorders and cognitive functions. Babiloni et al used directional network methods to detect the abnormal brain functions in amnesic mild cognitive impairment (MCI) and Alzheimers disease (AD); they demonstrated that directionality of parieto-to-frontal EEG synchronization was abnormal in AD and amnesic MCI 11. Winterer et al employed EEG-coherence to evaluate the frontotemporal structural connectivity and found that schizophrenic patients showed weaker frontal-temporal coherence 12. The study by 13 applied directional methods to evaluate the BFS of Stroop cognitive tasks; they found the anterior cingulate cortex was modeled by motor areas, and dorsal lateral prefrontal cortex was modeled by Brodmann 946 regions. Studies of EEG based brain network also suggested BFS features changed during a cognitive task, as well as under sleeping state 14, 15. Nevertheless, there are still few EEG-based effective network studies on heroin addiction.
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